When an advertiser provides a digital advertising plan (sometimes referred to as a campaign) to a digital content publisher, it is becoming more likely that the advertiser will request that a certain threshold of viewability is met during execution of the campaign. For example, an advertiser may request that when an ad runs on a publisher's website, it be viewable for 70% of the instances in which ads are shown. If the media campaign is called to deliver an ad 100,000 times (also known as ad impressions), the expectation would be that 70,000 of those ad impressions meet the viewability standard. Payment is often based on upon what the advertiser tracks via their reporting platforms, and the publisher may not have access to this information. This means that the publisher may not have a reliable way to deliver against nor optimize against the viewability threshold.
In order for digital content publishers to try to deliver digital media campaigns in accordance with the plan's defined viewability threshold, there are not many easy options. Publishers will often contract with a data or measurement provider. The workflow is to then pull daily reporting to see how a campaign is performing. The publisher's ad operation team can then retarget the campaign based on the reporting to deliver to only those “highly viewable” ad positions. For example, going back to the 100,000 ad impression order discussed earlier that has to hit 70% viewable impressions, the workflow in the prior art would be as follows; after the first day of the campaign, a report can be pulled from the publisher's ad vendor and viewability vendor of choice. After correlating the data, the publisher's ad operations team may then go into their ad delivery platform and restrict this campaign from delivering to certain underperforming ad locations since its underperformance is lowering the aggregate viewability performance of the campaign.
However, there are numerous problems with this method since a publisher is using relatively old data to make future predictions. In addition, in the world of online media a particular web page may gain or lose audience in significant swings depending on the public's interest in the content presented on that site for any given day. In addition, if a top level section such as “News” has an average viewability of 70% but different page templates or specific articles perform better or worse, there would be limited ability for the publisher to get granular targeting due to limitations in reporting and ad serving technology.
This document describes methods and systems that are directed to solving at least some of the issued described above.